fda.usc (version 0.9.8.1)

fdata2pls: Partial least squares components for functional data.

Description

Compute partial least squares (PLS) components for functional data.

Usage

fdata2pls(fdataobj,y,ncomp=2,...)

Arguments

fdataobj

fdata class object.

y

Scalar response with length n.

ncomp

The number of components to include in the model.

...

Further arguments passed to or from other methods.

Value

• fdata2pls function return:
• rotation
  fdata class object.
• x
  Is true the value of the rotated data (the centred data multiplied by the rotation matrix) is returned.
• fdataobj.cen
  The centered fdataobj object.
• mean
  mean of fdataobj.
• l
  Vector of index of principal components.
• C
  The matched call.

Details

The partial least squares are calculated by NIPALS algorithm.

References

Kraemer, N., Sugiyama M. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association. Volume 106, 697-705.

See Also

Used in: fregre.pls, fregre.pls.cv. Alternative method: fdata2pc.

Examples

n= 500;tt= seq(0,1,len=101)
x0<-rproc2fdata(n,tt,sigma="wiener")
x1<-rproc2fdata(n,tt,sigma=0.1)
x<-x0*3+x1
beta = tt*sin(2*pi*tt)^2
fbeta = fdata(beta,tt)
y<-inprod.fdata(x,fbeta)+rnorm(n,sd=0.1)
pls1=fdata2pls(x,y)
norm.fdata(pls1$rotation)